International Journal of Computer Applications (0975 – 8887) Volume 88 – No.11, February 2014 20 Scalable Video Compression by Employing TEMPO-SPA Arrangement along with Combined ADCT, Retaining- RLE Method Raghu K M Tech, Signal Processing REVA Institute of Technology ABSTRACT Motion based prediction used for video coding is an efficient method in the field of video compression. But the complexity and computation time involved in this method is a burden to apply them in real time applications. In this paper, an arrangement of video frames in temporal-spatial (TEMPO- SPA) domain, which is 3D to 2D mapping of video signals is proposed. As video signals are more redundant in temporal domain compared to spatial domain, the video frames are arranged in such a manner to exploit both temporal and spatial redundancies to achieve good compression ratio. In order to reduce the time and complexity of DCT computation, Approximated DCT (ADCT) is used along with combined Retaining-RLE method. ADCT is an approximation of DCT, whose transformation matrix contains most of them zeros which reduces the number of multiplications involved in the normal DCT computation. The quantized 8x8 blocks are then encoded by combination of Retaining and Run Length Encoding (RLE) methods. Out of 64 quantized coefficients in an 8x8 block, only certain number of coefficients is retained while zig-zag scanning order and RLE is applied to this retained sequence of coefficients to reduce the data in retained sequence. Thus providing high level of compression compared to previous compression standards. General Terms Scalable video coding, Video compression algorithms, Efficient video signal storage and transmission, Data compression, Digital Video processing. Keywords Approximate DCT, Low complexity video compression, TEMPO-SPA arrangement, Retaining-RLE compression, Video compression with higher compression ratio. 1. INTRODUCTION Nowadays in our digital world images and videos play a very important role in multimedia applications, but they contain huge amount of data. For example an image of resolution 1024x1024 requires 3145728 bytes of memory space to be stored. If a single image is very big to occupy in a memory device, then the videos which is a collection of such still image frames requires much more large memory space of 50GB range to store only a small movie clip! Hence compression of this huge amount of data in videos is very much essential. Compression is the process of compacting data into a smaller number of bits. Video compression (video coding) is the process of compacting or condensing a digital video sequence into a smaller number of bits. Compression involves a complementary pair of systems, a compressor (encoder) and a de-compressor (decoder). The encoder converts the source data into a compressed form (occupying a reduced number of bits) prior to transmission or storage and the decoder converts the compressed form back into a representation of the original video data. In block transform coding, a video is first divided into number of frames, each frame is considered as a still image and is compressed using a reversible, linear transform (such as Fourier transform). The entire video signal is converted into sequence of frames; each frame (image) is divided into non-overlapping blocks of equal size (8x8) and processing of these small blocks independently using 2-D transform is done. Linear transformations are used to map each block into a set of transform coefficients, which are then quantized and coded. For portable digital video applications, highly-integrated real-time video compression and decompression solutions are more and more required. Actually, motion estimation based encoders are the most widely used in video compression. Such encoder exploits inter frame correlation to provide more efficient compression. However, Motion estimation process is computationally intensive; its real time implementation is difficult and costly [3][4]. This is why motion-based video coding standard MPEG[11] was primarily developed for stored video applications, where the encoding process is typically carried out off-line on powerful computers. So it is less appropriate to be implemented as a real-time compression process for a portable recording or communication device (video surveillance camera and fully digital video cameras). In these applications, efficient low cost/complexity implementation is the most critical issue [4]. There are three types of data redundancies in video signal which can be eliminated to reduce the size of the video. The first type is Spatial Redundancy, which represents correlation between pixels within an image frame. This large amount of redundancy (high correlation) in an image frame is removed and can save a lot of data in representing the frame thus achieving compression. The second type is temporal redundancy, which represents correlation between pixels in successive frames in a temporal video sequence. Removing large amount of this redundancy leads to great deal of compression. Thus, researches turned towards the design of new coders more adapted to new video applications requirements. This led some researchers to look for the exploitation of 3D transforms in order to exploit temporal redundancy as well as spatial redundancy [5, 12]. 2. DEFINITIONS 2.1 Two dimensional Discrete Cosine Transform (2D-DCT) The Discrete Cosine Transform (DCT) is a time domain to frequency domain transformation. It has high energy packing